Creating SparseVector and setup with Spark

In this recipe, we examine several types of SparseVector creation. As the length of the vector increases (millions) and the density remains low (few non-zero members), then sparse representation becomes more and more advantageous over the DenseVector.

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
2. Import the necessary packages for vector and matrix manipulation:

import org.apache.spark.sql.{SparkSession}
import org.apache.spark.mllib.linalg._
import breeze.linalg.{DenseVector => BreezeVector}
import Array._
import org.apache.spark.mllib.linalg.SparseVector

3. Set up the Spark context and application...